The Experts below are selected from a list of 70728 Experts worldwide ranked by ideXlab platform
Lachlan L. H. Andrew - One of the best experts on this subject based on the ideXlab platform.
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Greening geographical Load Balancing
IEEE ACM Transactions on Networking, 2015Co-Authors: Zhenhua Liu, Steven Low, Minghong Lin, Adam Wierman, Lachlan L. H. AndrewAbstract:Energy expenditure has become a significant fraction of data center operating costs. Recently, “geographical Load Balancing” has been proposed to reduce energy cost by exploiting the electricity price differences across regions. However, this reduction of cost can paradoxically increase total energy use. We explore whether the geographical diversity of Internet-scale systems can also provide environmental gains. Specifically, we explore whether geographical Load Balancing can encourage use of “green” renewable energy and reduce use of “brown” fossil fuel energy. We make two contributions. First, we derive three distributed algorithms for achieving optimal geographical Load Balancing. Second, we show that if the price of electricity is proportional to the instantaneous fraction of the total energy that is brown, then geographical Load Balancing significantly reduces brown energy use. However, the benefits depend strongly on dynamic energy pricing and the form of pricing used.
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Geographical Load Balancing with renewables
ACM SIGMETRICS Performance Evaluation Review, 2012Co-Authors: Zhenhua Liu, Minghong Lin, Adam Wierman, Steven H Low, Lachlan L. H. AndrewAbstract:Given the significant energy consumption of data centers, improving their energy efficiency is an important social problem. However, energy efficiency is necessary but not suf- ficient for sustainability, which demands reduced usage of energy from fossil fuels. This paper investigates the feasibility of powering internet-scale systems using (nearly) entirely renewable energy. We perform a trace-based study to evaluate three issues related to achieving this goal: the impact of geographical Load Balancing, the role of storage, and the optimal mix of renewables. Our results highlight that geographical Load Balancing can significantly reduce the required capacity of renewable energy by using the energy more efficiently with “follow the renewables” routing. Further, our results show that small-scale storage can be useful, especially in combination with geographical Load Balancing, and that an optimal mix of renewables includes significantly more wind than photovoltaic solar.
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greening geographical Load Balancing
Measurement and Modeling of Computer Systems, 2011Co-Authors: Zhenhua Liu, Minghong Lin, Adam Wierman, Steven H Low, Lachlan L. H. AndrewAbstract:Energy expenditure has become a significant fraction of data center operating costs. Recently, "geographical Load Balancing" has been suggested to reduce energy cost by exploiting the electricity price differences across regions. However, this reduction of cost can paradoxically increase total energy use. This paper explores whether the geographical diversity of Internet-scale systems can additionally be used to provide environmental gains. Specifically, we explore whether geographical Load Balancing can encourage use of "green" renewable energy and reduce use of "brown" fossil fuel energy. We make two contributions. First, we derive two distributed algorithms for achieving optimal geographical Load Balancing. Second, we show that if electricity is dynamically priced in proportion to the instantaneous fraction of the total energy that is brown, then geographical Load Balancing provides significant reductions in brown energy use. However, the benefits depend strongly on the degree to which systems accept dynamic energy pricing and the form of pricing used.
Lee Ann Riesen - One of the best experts on this subject based on the ideXlab platform.
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a repartitioning hypergraph model for dynamic Load Balancing
International Parallel and Distributed Processing Symposium, 2009Co-Authors: Umit V Catalyurek, Doruk Bozdag, Erik G Boman, Karen Dragon Devine, Robert Heaphy, Lee Ann RiesenAbstract:In parallel adaptive applications, the computational structure of the applications changes over time, leading to Load imbalances even though the initial Load distributions were balanced. To restore balance and to keep communication volume low in further iterations of the applications, dynamic Load Balancing (repartitioning) of the changed computational structure is required. Repartitioning differs from static Load Balancing (partitioning) due to the additional requirement of minimizing migration cost to move data from an existing partition to a new partition. In this paper, we present a novel repartitioning hypergraph model for dynamic Load Balancing that accounts for both communication volume in the application and migration cost to move data, in order to minimize the overall cost. The use of a hypergraph-based model allows us to accurately model communication costs rather than approximate them with graph-based models. We show that the new model can be realized using hypergraph partitioning with fixed vertices and describe our parallel multilevel implementation within the Zoltan Load Balancing toolkit. To the best of our knowledge, this is the first implementation for dynamic Load Balancing based on hypergraph partitioning. To demonstrate the effectiveness of our approach, we conducted experiments on a Linux cluster with 1024 processors. The results show that, in terms of reducing total cost, our new model compares favorably to the graph-based dynamic Load Balancing approaches, and multilevel approaches improve the repartitioning quality significantly.
Anthony T. Chronopoulos - One of the best experts on this subject based on the ideXlab platform.
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Load Balancing in grid computing
Journal of Network and Computer Applications, 2017Co-Authors: Sumair Khan, Babar Nazir, Iftikhar Ahmed Khan, Shahaboddin Shamshirband, Anthony T. ChronopoulosAbstract:Grid computing is used to provide different services to users through resources that are geographically dispersed, dynamic, and heterogeneous in nature. In grid computing, Load Balancing plays a vital role in the re-allocation of user jobs when the grid resources become overLoaded. In the past few years, scores of Load Balancing strategies have been proposed by researchers to improve response time, communication overhead, throughput, and resource utilization. In this paper, surveyed Load Balancing strategies are divided into two broad categories, some supporting task migration and some of them having no support for task migration during the Load Balancing process. Each category is further categorized based on the basis of grid resource topology, that includes the flat resource topology and hierarchical resource topology. We discussed and compared the number of dynamic Load Balancing strategies related to these categories on the basis of different Load Balancing features and performance metrics.
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A truthful Load Balancing mechanism with verification
Parallel Processing Letters, 2006Co-Authors: Daniel Grosu, Anthony T. ChronopoulosAbstract:In this paper we investigate the problem of designing Load Balancing protocols in distributed systems involving self-interested participants. These participants have their own requirements and objectives and no a-priori motivation for cooperation. Their selfish behavior may lead to poor performance and inefficiency. To address this problem we design a Load Balancing mechanism with verification that provides incentives to participants to report their true parameters and follow the given algorithm. We prove that our Load Balancing mechanism is truthful (i.e., agents will be better off by reporting their true parameters) and satisfies the voluntary participation condition (i.e., truthful agents never incur a loss). We present a simulation study to show the performance of our Load Balancing mechanism.
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Noncooperative Load Balancing in distributed systems
Journal of Parallel and Distributed Computing, 2005Co-Authors: Daniel Grosu, Anthony T. ChronopoulosAbstract:In this paper, we present a game theoretic framework for obtaining a user-optimal Load Balancing scheme in heterogeneous distributed systems. We formulate the static Load Balancing problem in heterogeneous distributed systems as a noncooperative game among users. For the proposed noncooperative Load Balancing game, we present the structure of the Nash equilibrium. Based on this structure we derive a new distributed Load Balancing algorithm. Finally, the performance of our noncooperative Load Balancing scheme is compared with that of other existing schemes. The main advantages of our Load Balancing scheme are the distributed structure, low complexity and optimality of allocation for each user.
Xu Yue - One of the best experts on this subject based on the ideXlab platform.
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A Load-Balancing Model Based on Implementation Repository
Computer Engineering, 2004Co-Authors: Xu YueAbstract:After the existing Load-Balancing models are analyzed, a Load-Balancing model based on implementation repository is presented. Not only Loading balance is implemented through orienting the first request, but also Loading at a node is reduced through moving object and server program.
Zhenhua Liu - One of the best experts on this subject based on the ideXlab platform.
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Greening geographical Load Balancing
IEEE ACM Transactions on Networking, 2015Co-Authors: Zhenhua Liu, Steven Low, Minghong Lin, Adam Wierman, Lachlan L. H. AndrewAbstract:Energy expenditure has become a significant fraction of data center operating costs. Recently, “geographical Load Balancing” has been proposed to reduce energy cost by exploiting the electricity price differences across regions. However, this reduction of cost can paradoxically increase total energy use. We explore whether the geographical diversity of Internet-scale systems can also provide environmental gains. Specifically, we explore whether geographical Load Balancing can encourage use of “green” renewable energy and reduce use of “brown” fossil fuel energy. We make two contributions. First, we derive three distributed algorithms for achieving optimal geographical Load Balancing. Second, we show that if the price of electricity is proportional to the instantaneous fraction of the total energy that is brown, then geographical Load Balancing significantly reduces brown energy use. However, the benefits depend strongly on dynamic energy pricing and the form of pricing used.
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Geographical Load Balancing with renewables
ACM SIGMETRICS Performance Evaluation Review, 2012Co-Authors: Zhenhua Liu, Minghong Lin, Adam Wierman, Steven H Low, Lachlan L. H. AndrewAbstract:Given the significant energy consumption of data centers, improving their energy efficiency is an important social problem. However, energy efficiency is necessary but not suf- ficient for sustainability, which demands reduced usage of energy from fossil fuels. This paper investigates the feasibility of powering internet-scale systems using (nearly) entirely renewable energy. We perform a trace-based study to evaluate three issues related to achieving this goal: the impact of geographical Load Balancing, the role of storage, and the optimal mix of renewables. Our results highlight that geographical Load Balancing can significantly reduce the required capacity of renewable energy by using the energy more efficiently with “follow the renewables” routing. Further, our results show that small-scale storage can be useful, especially in combination with geographical Load Balancing, and that an optimal mix of renewables includes significantly more wind than photovoltaic solar.
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greening geographical Load Balancing
Measurement and Modeling of Computer Systems, 2011Co-Authors: Zhenhua Liu, Minghong Lin, Adam Wierman, Steven H Low, Lachlan L. H. AndrewAbstract:Energy expenditure has become a significant fraction of data center operating costs. Recently, "geographical Load Balancing" has been suggested to reduce energy cost by exploiting the electricity price differences across regions. However, this reduction of cost can paradoxically increase total energy use. This paper explores whether the geographical diversity of Internet-scale systems can additionally be used to provide environmental gains. Specifically, we explore whether geographical Load Balancing can encourage use of "green" renewable energy and reduce use of "brown" fossil fuel energy. We make two contributions. First, we derive two distributed algorithms for achieving optimal geographical Load Balancing. Second, we show that if electricity is dynamically priced in proportion to the instantaneous fraction of the total energy that is brown, then geographical Load Balancing provides significant reductions in brown energy use. However, the benefits depend strongly on the degree to which systems accept dynamic energy pricing and the form of pricing used.
